Early warning system - students at risk of failure
An open-source early warning system and workshop for students at risk of failing
Team members: David Hope, Pamela Docherty, Toby Bailey, Susan Rhind, Margaret Cullen, Avril Dewar, Helen Cameron
Student failure is a serious negative outcome, but can be predicted in advance. Many activities - such as low attendance at tutorials, or a failure to engage in routine administrative work - predicts poor academic performance in advance.
In this project, drawing on expertise from three Edinburgh schools across two colleges, we will develop a tool for predicting risk in advance and trial it on around 750 year 1 students. Students identified as at risk will be invited to a meeting with their tutor (or other relevant academic) and then referred to a workshop.
Following a systematic review and using local expertise, this workshop will be designed to improve ‘meta-cognitive’ skills, approaches to learning, motivation and identify pathways for student support.
Students who have performed well in year 1 will act as near-peer facilitators, and all staff and near-peers will have received training from the disability office/counselling service on helping students in need.
The intervention will run before the start of the December assessment diet.
Questionnaires and focus groups will be used to monitor student perception of the project. We will monitor the performance of participants against historic data to see if the intervention had an impact.
The tool will be disseminated across the university and beyond to any interested user, along with extensive documentation to allow the tool and workshop to be delivered in any local context.
Final Project Report
Final Report may be downloaded using link below:
Other Project Outcomes
- AMEE EWS Workshop 2015 (Poster)
- Open Source Early Warning Tool 2015 (Poster)
- Transit case study information
- Transit Workshop and training event outline 2015
- Slides from Gearing Up event : 'Building Supportive Communities by promoting student-led transition teams' (March 2016)
- Data Collection, identifying predictors [ Early Warning System : identifying and remediating students at-risk of failure before summative exams] (Paper, May 2015)